Circuitry dynamics underlying opioid-dependence: Integrating structural, functional, and transcriptomic mechanisms

PROJECT SUMMARY A range of cellular and circuit-level adaptations develops in response to chronic opioid exposure, which are strongly linked to several facets of opioid addiction: tolerance, withdrawal and processes that may contribute to compulsive use and relapse. However, we still do not have a comprehensive picture of the dynamic connections and activities of neuronal networks in the brain that express the opioid receptors and peptides. Therefore, a critical need exists to map the global cell-type identity, transcriptomic trajectory, shifting connectivity, and ensemble activity of the key opioidergic networks underlying the onset and maintenance of cellular dependence, and withdrawal. This proposal aims to investigate the architecture and function of endogenous MOR-expressing neural circuits in key cortical and subcortical brain regions, in order to determine how these circuits maintain cellular dependence and drive brain-wide maladaptive plasticity across different stages of the OUD cycle. In four complementary aims, we will first map the shifting structural and functional connectivity of opioidergic networks using viral-genetic and tissue clearing methods to identify monosynaptic inputs to all MOR-expressing, as well as withdrawal-active MOR-expressing neurons, as a function of opioid exposure and abstinence. We will then integrate these dynamic neuroanatomical maps with cell-type information and gene expression changes by combing single-nuclei sequencing and spatial cellular-resolution transcriptomics via hyper-multiplexed in situ hybridizations to generate the anatomic localization of hundreds of dependence-related genes, targeted to cell types and retro-labeled connections. Lastly, to reveal how MOR-expressing cells within the cortical and subcortical target regions are modulated during opioid exposure in real-time, we will use miniature head-mounted microscopes to image the neural ensemble activities across weeks of opioid exposure and withdrawal. To bridge these experimental measurements and provide a common framework for our analyses, we will adopt Network Control Theory to identify brain nodes that drive the transition between opioid dependence states to identify potential candidates that disproportionately drive each state. Our datasets will provide formal summaries and a publicly available, searchable database logging the activity, connectivity, and gene expression as they evolve with repetitive opioid exposure, withdrawal, and abstinence.